Integrating computational fluid dynamics into organ-on-chip systems: a glioblastoma-centred design and validation...

Area of Science:

• Biomedical Engineering
• Cancer Research
• Microfluidics

Background:

• Glioblastoma multiforme (GBM) is a lethal brain cancer with a complex tumor microenvironment (TME).
• Current organ-on-chip (OoC) models struggle to replicate GBM's biological complexity due to empirical design and lack of control over physical cues.
• Limitations exist in replicating GBM's biological complexity and in device fabrication/maintenance for OoC platforms.

Purpose of the Study:

• To review current limitations in GBM TME replication using OoC platforms.
• To highlight Computational Fluid Dynamics (CFD) strategies for enhancing OoC model design, precision, and biological fidelity.
• To propose a structured workflow for integrating CFD into GBM-on-chip model development and validation.

Main Methods:

• Comprehensive literature review of GBM, TME, OoC platforms, and CFD applications.
• Mapping of technical constraints in OoC fabrication and maintenance to specific CFD strategies.
• Synthesis of CFD integration workflows for GBM-on-chip model design, optimization, and validation.

Main Results:

• CFD offers powerful tools to overcome limitations in OoC design for GBM, enabling predictive control over flow, gradients, and mechanical cues.
• A structured workflow is proposed for integrating CFD into the development and validation of microfluidic GBM models.
• Validation frameworks are highlighted and mapped to GBM-on-chip applications, referencing international engineering and regulatory standards.

Conclusions:

• CFD is essential for advancing GBM-on-chip development, bridging engineering precision with biological complexity.
• Integration of CFD with AI-based optimization can lead to more predictive, scalable, and biologically relevant in vitro GBM models.
• This review provides a roadmap for leveraging CFD to improve the fidelity of microfluidic models for brain cancer research.